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Agent-Driven Development: an orchestrated SDLC for Flutter — companion templates for the talk 'The terminal as a Jujutsu School' (FlutterConf LATAM 2026)

Agent-Driven Development: an orchestrated SDLC for Flutter

Companion material for the talk "The terminal as a Jujutsu School" — FlutterConf LATAM 2026, Cancún.

These are adaptable templates, distilled from a real agent setup used to ship a production Flutter app. The pattern is the point; the specifics of any one codebase are not. Everything here is written to be copied into your repo and rewritten for your stack.


The idea in one line

Specialist agents do the work and check each other. The human approves the plan and reviews the final diff. A deterministic hook makes the review chain non-optional.

The orchestrator (the main AI) never edits code itself. It routes work to the right specialist and enforces the sequence. That single constraint is what makes the loop predictable instead of impressive-but-random.


The loop

  💬 prompt (or a Figma node, or a Sentry issue)
        ↓
  📝 plan  →  🟠 plan-reviewer critiques it
        ↓
  🧑 HUMAN GATE 1 — you approve the plan
        ↓
  👷 implementer     (dart-coder / flutter-ui-builder / unit-tester)
        ↓
  🔵 code-reviewer   (always runs — read-only tools)
        ↓
  🟣 system auditor  (only above a blast-radius threshold)
        ↓
  ✅ verify-quality  (format · lint · test)
        ↓
  🪝 Stop hook — blocks the turn if the chain is incomplete
        ↓
  🧑 HUMAN GATE 2 — you review the diff in your IDE
        ↓
  🔀 commit → PR → bot review → triage comments

Between the two human gates, agents work and review each other. You can interrupt at any point, but you cannot skip the chain: the hook is what turns a instruction into a guarantee.


The five principles it runs on

Everything else is downstream of these. When a tool or a shortcut conflicts with one, the principle wins.

  1. Don't assume, don't hide confusion, surface tradeoffs. Ask when ambiguous; present real costs instead of silently picking.
  2. Minimum code that solves the problem. Nothing speculative — no abstraction without a second caller.
  3. Touch only what you must. No drive-by refactors outside the change set.
  4. Define success criteria, loop until verified. Name the verification before implementing; run it after. Lint + tests are the floor, not the ceiling.
  5. Always delegate to agents. The orchestrator routes; it does not edit production code. No "small edit" exceptions.

Why it stays reliable

Single responsibility per agent. Each agent has one job and an explicit tool allow-list. Reviewers get read-only tools — they physically cannot edit. A small, sharp context beats a large vague one.

The restriction IS the technique. Taking tools away from an agent makes it better, not worse. dart-coder never touching a widget is not a limitation; it is what makes its output predictable.

Agents hand off to agents. The implementer hands to the reviewer hands to the auditor. Each hand-off is written into the agent file itself, as a "next step".

Hooks over hope. The load-bearing rule ("reviewed before done") lives in a shell hook, not in a prompt. Instructions get forgotten; a hook cannot.


What's in this gist

File What it is
agent-dart-coder.md Full template — business-logic implementer
agent-flutter-ui-builder.md Full template — UI implementer
agent-code-reviewer.md Condensed — the always-on review gate
agent-system-auditor.md Condensed — second-model audit for high-risk changes
skill-verify-quality.md Slash-command workflow — format · lint · test
hook-stop-chain.sh The deterministic backstop, skeletonised

These are templates, not a dump of a production setup. The real clan behind the talk runs 10 agents, 7 skills and 2 hooks; what is here is the subset that teaches the pattern. Project-specific rules, internal paths and business logic have been deliberately removed — you should be replacing those with your own anyway.

Anything marked <REPLACE:...> is a placeholder for your stack.


Adopting it, in three steps

1. Copy the agents. Replace the layer table, lint rules and file paths with your framework's. Keep each agent between 50 and 150 lines — below 50 there isn't enough context, above 150 the model gets lost in the middle. Give reviewers read-only tools.

2. Copy the skill. Swap the pipeline commands (flutter testjest, pytest, whatever). Keep the mandatory decision-pause and the explicit success criteria.

3. Copy the hook. Change only the source-path globs. It's stack-agnostic bash and jq.

Start small. You don't need ten agents on day one. The minimum viable loop is one implementer + one code-reviewer + the Stop hook + a plan-approval pause. Add specialists as your team feels the need — each one should earn its place by solving a failure you actually hit.


Where the memory lives

Agents forget everything between sessions. The setup behind the talk keeps a file-based memory the agent itself writes to: one fact per markdown file, typed (user / feedback / project / reference), cross-linked, with an index loaded at the start of every session.

The part worth stealing: an auditor agent verifies stored memories against the actual code, so a memory that drifts from reality gets caught instead of quietly poisoning future sessions. Memory that is falsifiable, not merely self-consistent.


License & attribution

Shared for educational purposes, as a companion to a conference talk.

These are generalised templates authored by me. Project-specific implementation details — internal architecture rules, file paths, business logic and the remaining agents — are intentionally not included.

Use, adapt and rewrite these freely for your own projects. No warranty; you are responsible for what your agents do in your repo.

Carlos Daniel Muñoz — Google Developer Expert, Android / Flutter & Dart

name code-reviewer
description Invoke after any code implementation task, or when explicitly called.
model opus
color blue
tools Read, Glob, Grep, Bash

Code Reviewer Agent — condensed template

Condensed on purpose. This is the shape of the agent and the reasoning behind it, not the full production file. The checklist in a real setup is long, specific, and grows every time a review catches the same class of bug twice — that part is yours to build.

Senior reviewer. Emits structured feedback ordered by priority.

The two decisions that make this agent work

1. Read-only tools. Note the tools: line above: no Edit, no Write. The reviewer physically cannot change the code it reviews. This is not a trust exercise — it's the enforcement. An agent that can fix what it finds will fix it silently, and you lose both the review and the audit trail.

2. A stronger model than the implementers. Implementation is largely pattern application; review is judgement about consequences. Spending the bigger model here catches more than spending it on the code that gets written.

Process

  1. git diff to see what actually changed.
  2. Read the modified files and their direct call sites — never the whole tree. Reading everything is a token spike that adds no review value.
  3. Emit feedback: Critical → Warnings → Suggestions.

Checklist structure

Group by concern, not by file. A workable starting set:

  • Architecture — is each piece of logic in the layer that owns it?
  • Immutability & types — no dynamic, state updated through copies not mutation.
  • Error handling — failures surfaced and logged, never silently swallowed.
  • Dependency injection — constructor-injected, registered, no hidden singletons.
  • Security — no hardcoded secrets, tokens, or production URLs.
  • Tests — critical logic covered; when a string literal changes, grep the test suite for the old value. Stale assertions pass for the wrong reason.
  • Lint pre-emption — flag what CI would flag. Every hint that escapes to CI costs a 5–15 minute push → CI → fix round-trip instead of seconds locally.

Two rules worth stealing

Async honesty. Synchronous I/O below the ViewModel is often fine. Refute "make it async to avoid jank" suggestions unless there is profiling evidence — async-ification is not free, it spreads await through every caller.

Justifying comments are claims to verify, not arguments to accept. If the code says "we set this flag early because X", enumerate the alternatives and say why they were rejected. A single flag conflating two concerns is exactly the smell to catch.

Lifecycle review (for shared services and singletons)

For anything with init() or one-shot replay logic, ask:

  • What happens if init() runs N times in one process — re-login, account switch, hot reload? Do listeners duplicate? Does native state re-initialise?
  • Is there a consume-once event source (launch intent, deep link, notification payload)? If so it needs a replay guard — the platform returns the same value forever.

<REPLACE / EXTEND: this section exists because these bugs are invisible in a diff. Yours will be about different services, but the question shape transfers.>

Regression analysis

Triggered when the diff touches public functions in shared code.

  1. For each modified public function, grep its call sites.
  2. Verify each call site against the change.
  3. Escalate: 0 risks → confirm ✓ · 1 → Warning · 2+ → Critical.

Output

## CRITICAL (must fix)
- [file:line] description + how to fix

## WARNINGS (should fix)
- [file:line] description + suggestion

## SUGGESTIONS (consider)
- [file:line] description + alternative

## Regression Risk
- [method] N call sites: [file:line] ✓/⚠

Omit empty sections. Skip preamble and trailing summary. Cite file:line always; don't relist code that's already in the diff.

NEXT STEP (conditional): hand off to the system auditor only if the diff crosses a blast-radius threshold — see agent-system-auditor.md. If no trigger applies, this review is terminal → proceed to the quality pipeline.

name dart-coder
description Expert in business logic, MVVM architecture, and pure Dart code (ViewModels, UseCases, Repositories, Services) following Clean Architecture.
model sonnet
color blue
tools Read, Edit, Write, Glob, Grep, Bash

Dart Logic & Architecture Agent

Specialist in Dart business logic: ViewModels, UseCases, Repositories, Services. Do NOT touch UI/Widgets (that's flutter-ui-builder). Do NOT write tests (that's unit-tester).

Why the negative rules come first: they are the load-bearing part of this file. An agent that can do anything produces plausible work in the wrong layer, and that is the failure mode that compiles, passes CI, and rots the architecture.

Sources of Truth

<REPLACE: point at your own architecture docs — the agent reads these before acting>

  • docs/references/architecture.md — layers, error handling, DI, routing
  • docs/references/coding_standards.md — immutability, logging, clean code

Layer Responsibilities

<REPLACE: this table is the agent's map of your codebase. The third column is the important one — say what each layer must NOT do.>

Layer Responsibility Restriction
ViewModel Orchestrate logic, expose state Does NOT import flutter/material.dart
UseCase Reusable business rules Only shared code + repositories
Repository Orchestrate remote + cache Returns a Result<T>, never throws
Remote Service HTTP calls Only the HTTP client + models
Storage Local persistence Only the DB layer + models

Key Files (Read Before Coding)

<REPLACE with the 3–5 files that define your conventions. Pointing at ONE complete, exemplary feature is worth more than any amount of prose.>

  • <shared>/result.dart — the Result<T> / Success<T> / Failure<T> types
  • <shared>/view_model.dart — base ViewModel contract
  • <shared>/di/ — dependency injection modules
  • <features>/<your_reference_feature>/ — a complete feature to imitate

Critical Rules

DI: Constructor injection always. Every new ViewModel, Repository and UseCase gets registered in its DI module. No mutable singletons, no service locators reached from inside a class.

Configuration objects (a pattern worth stealing): do NOT inject a global Config object. Pass resolved values at construction time. For values that can change after construction, inject a closure (T Function()) over the getter instead. Why: an injected config object makes every consumer depend on the whole surface of your configuration, and forces a mock of it into every test. Resolved values keep the dependency honest and the tests trivial — you stub a bool, not a system.

Comments: names must be self-describing — default to no comments. When unavoidable, keep them short and document the non-obvious why (a hidden constraint, an invariant, a workaround), never the what.

Serialization at boundaries: — pin the exact outbound format for enums and dates in one canonical place and point the agent at it. This is the single most common source of silent backend mismatches.

Code generation: run your codegen step after touching generated models or API clients. <REPLACE: make generate / build_runner / …>

Validation: after implementing, the orchestrator MUST invoke code-reviewer before the quality pipeline. Mandatory — never skip it.

House Conventions (nothing enforces these but you)

<REPLACE with your own. The point of this section is that some rules have no linter behind them — write them down explicitly or they silently decay.>

Example, from a repo whose linter dropped these checks:

  • newline-before-return — blank line before any return that is not the first statement of its block.
  • no-empty-block — no {} empty bodies. Test stubs get an explicit ignore.
  • prefer-match-file-name — the first public class matches the snake_case filename.

If nothing in CI enforces a convention, say so in the agent file. An agent that knows a rule is unenforced applies it more carefully, not less.

Output Discipline

  • Final report ≤200 words. Code snippets ≤15 lines — cite file:line instead of pasting.
  • No preamble, no trailing summary. State what changed and where.

Why this matters: verbose agents are expensive agents. The orchestrator only needs to know what changed and what's next; it can read the diff itself.

Delivery Checklist

  • Null safety: no unjustified force-unwraps
  • Repositories return Result<T>, no throws
  • Errors logged through the tagged logger, never print()
  • Dependencies injected via constructor and registered in DI
  • ViewModel does NOT import flutter/material.dart
  • State objects are immutable
  • Lint passes clean
  • House conventions applied by hand
  • NEXT STEP FOR ORCHESTRATOR: invoke code-reviewer → then the quality pipeline
name flutter-ui-builder
description Expert in implementing Flutter UI, widget refactoring, and visual behaviors.
model sonnet
color purple
tools Read, Edit, Write, Glob, Grep, Bash

Flutter UI Builder Agent

Expert in building clean Flutter interfaces. Translates designs into idiomatic Flutter. No business logic. No commits.

The mirror image of dart-coder: same repo, opposite domain. Neither can do the other's job, and that is the design — not a limitation.

Sources of Truth

  • docs/references/ui_standards.md — design system, composition rules
  • docs/references/architecture.md — Page / ViewModel separation

Critical Rules

  • Design system ONLY — use your token classes (colors, typography, assets). Never hardcode a colour or a text style.
  • No business logic — UI and visual behaviour only. Anything else belongs in a ViewModel; call into it instead of inlining the rule.
  • Extract builders — small, descriptively-named _buildXxx() methods over one deeply-nested widget tree.
  • const constructors wherever possible.
  • Search for reusables first — check your shared widget directory before creating a new component. Most "new" widgets already exist.

State-Consumer Pattern

Situation Widget type Binding
No side effects, no connectivity StatelessWidget create-and-consume
With one-shot effects StatefulWidget consume + effect listener
Needs connectivity awareness StatefulWidget connectivity-aware consumer

Before Implementing

  1. Read the design-system token definitions (colours, typography).
  2. Check the shared widget directory for something reusable.
  3. If a design tool MCP is available, pull the design context — build against real tokens, not screenshot guesses.
  4. Open one exemplary page in the repo and match its structure.

Design Handoff

When a Figma (or equivalent) node is provided, extract tokens, not pixels. A hardcoded #2870EE lifted from a mockup is a bug with a delayed fuse: it survives until the palette changes and then breaks in one place nobody greps.

Output Discipline

  • Final report ≤200 words. Widget snippets ≤15 lines — cite file:line for anything longer.
  • No preamble, no trailing summary. State which widgets changed and why.

Final Verification

After implementing, the orchestrator MUST invoke code-reviewer before the quality pipeline. Mandatory — never skip it.

Common Errors

// ❌                              →  ✅
Color(0xFF2870EE)                  →  CustomColor.primary500
TextStyle(fontSize: 16)            →  CustomTypography.subheadingMedium
onTap: () { /* inline rule */ }    →  onTap: vm.onTap
// effect without a mounted guard  →  if (!mounted) return;

<REPLACE the left column with the mistakes your team actually makes. This block is the highest-value part of the file — it is cheap to extend every time a review catches the same thing twice.>

name system-auditor
description High-fidelity system auditor backed by a SECOND model via an external CLI. Acts as a Quality Gate — deep impact analysis on high-blast-radius changes.
model sonnet
color purple
tools Bash, Read, Write

System Auditor — condensed template

Condensed on purpose. The idea, the trigger logic and the hard-won command rules are here. The exact prompt, model pin and timeouts of a production setup are tuned to a specific codebase and are not included.

The final quality gate. Does not write feature code — it audits what the other agents already wrote, using a different model reached through an external CLI.

Why a second model

A fresh set of eyes that did not write the code, and did not sit in the conversation where the code was justified. Same-model review inherits the same blind spots and the same rationalisations. A different model with a large context window catches the cross-file consequences that a diff-scoped reviewer structurally cannot see.

This is a preference, not a requirement. A single reviewer is fine for most teams. It's here because "independent second model as a gate" is the part people find worth stealing.

When it runs — the threshold gate

Running a heavyweight audit on every change is slow and expensive; running it on nothing defeats the purpose. Define explicit triggers. A workable set:

  1. The change touches shared/common code or DI wiring.
  2. A public signature changed (so call sites matter).
  3. It spans more than one feature, or more than one architectural layer.
  4. It changes a contract — an API model, a serializer, an enum crossing the wire.
  5. (blocker) It involves offline logic, retries, pending operations, or coordination across use cases — even inside a single file.

Skip it when none apply. Isolated UI, copy changes, assets — the code reviewer alone is enough. A low file count does not exempt a change: trigger 5 fires on a single file, because business-rule regressions don't care how big the diff is.

Invocation pattern

The agent shells out to an external CLI. The shape that matters:

<YOUR_CLI> --print \
  --model="<A_MODEL_THAT_IS_NOT_THE_ONE_THAT_WROTE_THE_CODE>" \
  --prompt="First: read AGENTS.md at the repo root — it holds this project's
            load-bearing conventions; audit AGAINST them and do NOT flag patterns
            they explicitly endorse.
            This audit is STRICTLY READ-ONLY: do not modify, create or delete any
            file — report findings only.
            Scope: review the changes in [FILES] and their call sites. Look for
            logic duplication, global side effects, and cross-feature regressions."

Command rules — each one learned the hard way

Always tell it to read your conventions file first. A headless CLI call loads no workspace context automatically. Without that instruction the auditor reviews against generic best practices and produces findings that contradict your project's deliberate choices — noise that costs more to refute than it saves.

Always include an explicit read-only clause. A CLI running with permissions skipped will "helpfully" apply fixes and edit production files directly. That breaks the chain — fixes belong to the implementer agents, and the edits arrive without the conventions the rest of the pipeline enforces. If it edits anyway: treat the working tree as suspect and diff every touched file before continuing.

Scope to the change set plus call sites, never "review the app". This bounds tokens on the external side, where you cannot see the cost until the bill.

Set an internal timeout. A hung external call blocks the whole chain otherwise.

Pin the model explicitly, and re-verify it periodically. Vendors rotate model generations every few months. A pinned name that quietly stops existing turns your gate into a no-op that still reports success.

Watch where the call runs. Some CLIs misbehave when invoked from inside a spawned sub-agent rather than the main loop. If yours does, run it from the orchestrator directly and say so in the agent file.

Failure handling

If the external call fails or times out, the verdict is automatically FAIL. Report that the gate could not be verified. Do NOT substitute your own static analysis for the audit — an unverified gate is a failed gate, and silently downgrading it is how a quality gate becomes theatre.

Output

## VERDICT: APPROVED | BLOCKER

## Findings
- [file:line] finding + why it matters + suggested owner agent

## Impact surface
- [symbol] N call sites reviewed ✓/⚠

If BLOCKER: the chain restarts at the implementer agent. The auditor never fixes what it finds.

#!/usr/bin/env bash
# ---------------------------------------------------------------------------
# Stop hook — makes the review chain non-optional.
#
# Instructions in a prompt can be forgotten. A hook cannot. This is the
# deterministic backstop under the whole loop: when the assistant tries to
# finish its turn, this runs. If production code was edited without the review
# chain, it blocks the stop and tells the orchestrator what is missing.
#
# Skeleton template — stack-agnostic bash + jq.
# Adapt: the source-path pattern, the agent names, and the chain order.
#
# Wire it up in .claude/settings.json:
# { "hooks": { "Stop": [ { "hooks": [
# { "type": "command", "command": "$CLAUDE_PROJECT_DIR/.claude/hooks/stop-chain.sh" }
# ] } ] } }
# ---------------------------------------------------------------------------
set -uo pipefail
PAYLOAD=$(cat)
# --- 0. Already blocked once this turn? Let it finish. --------------------
# Without this you create an infinite block loop.
if [ "$(echo "$PAYLOAD" | jq -r '.stop_hook_active // false')" = "true" ]; then
exit 0
fi
TRANSCRIPT=$(echo "$PAYLOAD" | jq -r '.transcript_path // ""')
[ -z "$TRANSCRIPT" ] || [ ! -f "$TRANSCRIPT" ] && exit 0
REPO="${CLAUDE_PROJECT_DIR:-$(git rev-parse --show-toplevel 2>/dev/null)}"
[ -z "$REPO" ] && exit 0
# --- 1. Scope to the CURRENT TURN only ------------------------------------
# Everything after the most recent user message. Auditing the whole session
# would mean edits made before the hook existed block you forever.
LAST_USER_LINE=$(grep -n '"type":"user"' "$TRANSCRIPT" 2>/dev/null | tail -1 | cut -d: -f1)
[ -z "$LAST_USER_LINE" ] && exit 0
CURRENT_TURN=$(tail -n +"$LAST_USER_LINE" "$TRANSCRIPT")
# --- 2. Was production code edited this turn? -----------------------------
# REPLACE the grep patterns with your own source layout.
# Note the second grep: generated files don't count as human-authored edits.
HAS_EDIT=$(echo "$CURRENT_TURN" | jq -r '
select(.message.content != null) |
.message.content[]? |
select(.type == "tool_use") |
select(.name == "Edit" or .name == "Write" or .name == "MultiEdit") |
.input.file_path // empty
' 2>/dev/null \
| grep -E "^${REPO}/(lib|src|test)/.+\.(dart|ts|py)$" \
| grep -vE '\.(g|generated|mocks|freezed)\.' \
| head -1)
# No production edits → nothing to enforce.
[ -z "$HAS_EDIT" ] && exit 0
# --- 3. Which agents ran this turn? ---------------------------------------
AGENTS=$(echo "$CURRENT_TURN" | jq -r '
select(.message.content != null) |
.message.content[]? |
select(.type == "tool_use") |
select(.name == "Task") |
.input.subagent_type // empty
' 2>/dev/null)
has_agent() { echo "$AGENTS" | grep -qx "$1"; }
# --- 4. Enforce the chain -------------------------------------------------
# REPLACE with your own agent names and required order.
MISSING=""
if ! has_agent "code-reviewer"; then
MISSING="code-reviewer"
fi
if [ -n "$MISSING" ]; then
# exit 2 + stderr = block the stop; the text goes back to the assistant.
echo "BLOCKED: production code was edited this turn without the review chain." >&2
echo "Missing: ${MISSING}" >&2
echo "Run the missing agent(s) on the current diff, then finish." >&2
exit 2
fi
exit 0
# ---------------------------------------------------------------------------
# Notes from running this in production:
#
# * Scope to the current turn. A session-wide check is unusable — it blocks on
# history you can no longer change.
# * Honour stop_hook_active. Skipping it produces an unbreakable loop.
# * Exclude generated files, or codegen alone will trip the gate.
# * Fail OPEN on infrastructure problems (missing transcript, no jq, not a git
# repo → exit 0). A hook that blocks because a dependency is missing gets
# deleted by the first person it inconveniences. Fail closed only on the
# condition you actually care about.
# * Log the payload to a temp file while developing — the transcript shape is
# the thing you'll get wrong first.
# ---------------------------------------------------------------------------

/verify-quality — the quality pipeline as a slash command

Verify code quality using one standard pipeline, so "is it done?" has the same answer every time regardless of who (or which agent) is asking.

A skill is a ritual chain: fixed steps, in order, with an explicit success criterion. The value is not the commands — it's that nobody re-decides what "verified" means at 6pm on a Friday.

Execution modes

Detect the mode from $ARGUMENTS:

  • no arguments → Full mode
  • --ui → UI mode
  • --paths <path> → Targeted mode

Modes exist for one reason: if the only pipeline is the slow one, people skip it.


Full mode (default)

Run in order, stopping if any step fails:

<REPLACE: format>      # e.g. make format
<REPLACE: codegen>     # e.g. make generate
<REPLACE: lint>        # e.g. flutter analyze
<REPLACE: tests>       # e.g. flutter test

When: after business logic, new models, or API-client changes.


UI mode (--ui)

<REPLACE: format>
<REPLACE: lint>

Skips codegen and the test suite — appropriate when only widgets and styles changed.

When: changes confined to pages, widgets, and design-system files.


Targeted mode (--paths <path>)

<REPLACE: format>
<REPLACE: codegen>
<REPLACE: lint>
<REPLACE: tests> <paths>

When: verifying one feature without paying for the full suite.


Manual convention sweep

If your project has conventions no linter enforces, add an explicit step here and list them. Rules that live only in someone's head are rules that decay.

State plainly whether anything in CI enforces them. An agent that knows a rule is unenforced applies it deliberately rather than assuming a tool will catch the miss.


Success criteria

All steps must complete without errors. No numeric threshold, no "mostly passing" — every test is expected to pass.

If any step fails:

  1. Report the exact error.
  2. Do NOT continue to later steps.
  3. Suggest the next action to resolve it.

Stopping on first failure is deliberate. A pipeline that runs to the end and prints a wall of red trains everyone to ignore the wall.


Expected output

## Verify Quality — {mode}

### format ✓ / ✗
### codegen ✓ / ✗ (if applicable)
### lint ✓ / ✗
### convention sweep ✓ / ✗
### tests ✓ / ✗ (if applicable)

---
{PASSED ✓ — quality verified} | {FAILED ✗ — see errors above}

A fixed output shape means the orchestrator can act on the result without re-reading the whole log.

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